Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “M87”
Time-resolved reconstruction of M87*
<p>The dataset contains 160 approximate posterior samples of the time-variable shadow of M87*</p> <p>Details can be found in readme.txt</p>
VirgoA M87 Heat Transfer
<p>The research continued from the white hole observation experiment with data analysis. The math- ematical method derived before the observation is supplemented with the heat transfer method. The research took an empirical boundary approach on heat flux for the heat transfer between a black hole and white hole, without considering the Big Bang theory. The heat transfer in the system material boundary is seen as a gravitational indicator from asymptotic decay. Albeit the numerical results are instrumentation specific, the conceptualization of the method can be applied to other data imaging apparatus. However, this method is material dependent, and the estimation of the imaginary time di- mension depends on the chemical components of incident light and detection plate. It puts the cosmic microwave background to the account of instrumentation noise, and conceptualized cosmic background radiation for asymptotic safety. The convergence and divergence can be further corrected by different bias factors.</p>
20130430_006_M87_Rec_20
<p>User: Elodie Ey - email: eye@igbmc.fr<br> Mouse: M87 (group re-20 cage conditions study) - Sex: male - Strain: C57BL/6J - Genotype: +/+ - Treatment: none<br> Protocol: Male/male interaction – rectangle cage (20 min + 8 min) - File number: 1<br> Upon arrival, seven-week old C57BL/6J male mice were housed in groups of four in a colony room maintained at 23±1°C on a 12:12 hour light/dark cycle, with lights on at 8:00 AM. Mice had access to food and water ad libitum. One week after their arrival, the 48 males considered as occupants were socially isolated for 3 weeks before the experiments to increase their social motivation and reduce their aggressiveness. The 12 mice used as new-comers were kept in groups of four males throughout the experiment. The experiments were conducted when animals were 11 weeks of age. The day of testing, the tested mouse was left 20 min for habituation in the rectangle test cage (Plexiglas, 50×25×30 cm; 100 lx; clean sawdust bedding; sound-proof chamber). After this time, an unfamiliar C57BL/6 mouse of the same sex was introduced. The two animals were allowed to freely interact for 8 min. Vocalisations were recorded with a Condenser ultrasound microphone Polaroid/CMPA, the interface UltraSoundGate 416-200, and the software Avisoft SASLab Pro Recorder from Avisoft Bioacoustics (Berlin, Germany; sampling frequency: 300 kHz; FFT-length: 1024 points; 16-bit format). Simultaneously, social interactions were videotaped continuously (high-resolution CamTech Super-Hi-Res video camera; 25 fps; starting point for synchronization: the moment the hindlegs of the new comer touched the ground).<br> Number of audio files for each individual: 1 (the second file is the video file of the interaction)</p> <p>Experiment: cage conditions (20 min hab, rectangle)<br> Date experiment: 2013-04-30<br> Temperature: 22°C<br> Light cycle: day<br> Microphone: Avisoft Polaroid/CMPA<br> Acquisition hardware: Avisoft UltraSoundGate 416-200<br> Acquisition software: Avisoft-Recorder USG 4.2.15<br> Sampling frequency: 250<br> Bit depth: 16</p> <p>Laboratory: Institut Pasteur<br> 25, rue du Dr Roux<br> 75015 Paris<br> France</p>
Co-added optical spectra of globular clusters around M87 in the Virgo Cluster
<p>This dataset provides the co-added optical spectra of globular clusters in the Virgo core region. The spectra were used in the stellar population analysis of the paper "The Next Generation Virgo Cluster Survey. XXXIII. Stellar Population Gradients in the Virgo Cluster Core Globular Cluster System" by Ko et al. (2022). The detailed description can be found in the README file.</p>
Data files for "Curvature in the very-high energy gamma-ray spectrum of M87"
<h1>Summary</h1> <p>In this repository, we provide some auxiliary material in connection to our paper “Curvature in the very-high energy gamma-ray spectrum of M87" accepted for publication in the Astronomy and Astrophysics (A&A) Journal and available on Arxiv through the ID <a href="https://arxiv.org/abs/2402.13330" target="_blank" rel="noopener">arXiv:2402.13330</a>. For the full list of authors, please refer to the paper.</p> <p>In the publication, we study the very-high energy gamma-ray spectrum of a stacked high emission state of M87 using H.E.S.S. observations. We detect a curvature in the spectrum that is not related to the EBL absorption. In addition to that, we show that the gamma-gamma absorption by star light from the galaxy is weak to explain the measured curvature and that it is unlikely that different high states with similar spectral distribution could be able to explain the same curvature.</p> <h1>Data and example code</h1> <h2>ECSV tables</h2> <p>The ecsv tables provide the means to reproduce the figures found in the paper. They can be opened with `astropy.QTable` as demonstrated below.</p> <pre><code>from astropy.table import QTable table = QTable.read('Fig1_lightcurve_table.ecsv') print(table)</code></pre> <p>The following example shows how to reproduce Fig. A2 from the paper (the modules imported are needed in the loaded enviroment):</p> <pre><code>from astropy.table import QTable from scipy.stats import gmean %matplotlib inline import matplotlib.pyplot as plt import numpy as np import seaborn as sns from ebltable.ebl_from_model import EBL cmap = sns.color_palette("colorblind", as_cmap=True) colors = sns.color_palette("colorblind", 6) ebl = {} for m in ["finke2022", "kneiske", "dominguez-upper"]: ebl[m] = EBL.readmodel(m) lmu = np.logspace(-1,3.,100) z = 0.0042 nuInu = {} for m, e in ebl.items(): nuInu[m] = e.ebl_array(z,lmu) nuInu table = QTable.read('FigA2_EBL_ULs.ecsv') wavelengths = table["Wavelength"].value wavelengths = wavelengths counter = 0 for m in ["finke2022", "kneiske", "dominguez-upper"]: plt.loglog(lmu,nuInu[m], lw = 2.,label=f"{m} UL", color=colors[counter] ) ULs = table[f"{m} UL"][(wavelengths>12.4)*(wavelengths<40)].value plt.loglog(wavelengths[(wavelengths>12.4)*(wavelengths<40)],ULs, lw = 2., label = f"UL (this work)", ls='dashed', color=colors[counter]) plt.arrow(gmean(wavelengths[(wavelengths>12.4)*(wavelengths<40)]), np.median(ULs), 0, -0.2*np.median(ULs), head_width=5 ,head_length=0.1*np.median(ULs), alpha=0.5, color=colors[counter]) counter+=1 plt.gca().set_xlabel('Wavelength ($\mu$m)',size = 'x-large') plt.gca().set_ylabel(r'$\nu I_\nu (\mathrm{nW}\,\mathrm{sr}^{-1}\mathrm{m}^{-2})$',size = 'x-large') plt.legend(loc = 'lower center', ncol = 2) plt.tight_layout() plt.show()</code></pre> <h2>Text files</h2> <p>The text files provide the gammapy fit results for the various analyses in the main text of the paper. For a complete definition of the models, we refer the reader to the paper. The name of the file is given by <em>fit_stacked_M87_<strong>MODEL</strong>_flux_90perc_<strong>ENERGYRANGE</strong>_90perc.txt</em>, where <strong>MODEL</strong> is the spectral model fitted (e.g., <em>PLxEBLfinke2022</em> or <em>PLxEBLfinke2022-free</em> in case the EBL intensity alpha_norm is a free parameter) and <strong>ENERGYRANGE</strong> is the energy range of the reduced dataset (e.g., <em>0.3_31.6TeV</em>).</p> <p> </p>
The simulation data of paper "Modeling the inner part of the jet in M87: confronting jet morphology with theory"
Open the record for dataset details and reuse information.
The simulation data for the paper: Modeling the inner part of the jet in M87: confronting jet morphology with theory
<p>"mad98.prim.02740.athdf" is the simulation data of the fiducial model MAD98, "mad98low.prim.03900.athdf" is the simulation of the low resolution of MAD98.</p>
DATA OF M87
<p>The simulation data of the fiducial MAD98 model.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.